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標題: 評估氣候門檻對人類健康與農業之影響
Essays on the Impacts of Climate Threshold on Human Health and Agriculture
作者: 陳品宇
Chen, Ping-Yu
關鍵字: 經濟-能源-環境關係
Economic-Energy-Environmental Nexus
Temperature Threshold
Growing Degree Days
Panel Smooth Transition Regression Model
出版社: 應用經濟學系所
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摘要: 近年來,不論是已開發或開發中國家其溫室氣體排放量隨著工業發展增加幅度劇烈,尤其是占溫室氣體排放量超過一半的二氧化碳排放量是造成氣候變遷主要原因。政府間氣候變遷小組(Intergovernmental Panel on Climate Change,簡稱IPCC)2007年報告中指出溫室氣體排放量相較於2000年的排放水準將會增加25%至90%,而與能源有關的二氧化碳排放量則會增加40%至110%。因此,如何面對因氣候變遷所帶來的威脅是全球首要的任務。由於二氧化碳排放量對人類的影響是全球性而非地域性,首先,利用Panel Cointegration與Vector Error-Correction Model討論全球(188個國家)1993年到2010年期間經濟-能源-環境三者間的動態關係。接著,考量到不同的經濟發展程度探討經濟-能源-環境之間的關係。實證結果指出全球,已開發或開發中國家的國內生產毛額(Gross Domestic Product,簡稱GDP),能源消費,與二氧化碳排放量三者間均存在長期均衡關係,能源消費增加則造成二氧化碳排放量增加,GDP與二氧化碳排放量兩者的關係則存在環境顧志耐曲線(Environmental Kuznets Curve,簡稱EKC),即GDP與二氧化碳排放量之間為倒U字型的關係。就短期因果關係實證結果,已開發國家呈現能源消費對二氧化碳排放量與GDO對能源消費的單一因果關係,而GDP與二氧化碳排放量則互為因果。然而,開發中國家呈現能源消費對二氧化碳排放量的單一因果關係,而二氧化碳與能源消費則分別與GDP互為因果。 IPCC 2007年報告同時也認為全球溫度上升導致熱浪發生頻率增加,並預測未來極端氣候事件發生的頻率與強度會越來越嚴重,因此找出氣候門檻對人類社會的影響刻不容緩,以幫助我們面對極端氣候事件的影響之不確定性。一開始利用Panel Threshold Model去檢定22個OECD國家的78個主要城市在1990年至2008年期間溫度與死亡率之間是否存在門檻關係,實證發現,溫度與死亡率間存在三個門檻效果,即不同的溫度門檻(-9.33℃,8.32℃,以及30.85℃)對死亡率有不同的影響。當溫度超過30.85℃,高溫則會造成死亡率的增加。當溫度在30.85℃與8.32℃之間,溫度對死亡率則無顯著的影響。當溫度在8.32℃與-9.33℃之間與小於-9.33℃時,溫度降低對人類生命健康產生危害。根據溫度對死亡率的彈性以及未來可能的溫度情境預測未來氣候變遷在不同的緯度區(低於30°,31°-40°,41°-50°,51°-60°,61°-70°)於2021-2040,2041-2060,2061-2100期間內對死亡率的影響。發現在41°-50°與51°-60°的緯度區內夏季死亡率增加的速度遠大於其他緯度區,而冬季死亡率相較於其他緯度區也呈現明顯下滑的趨勢。 接著,探討氣候門檻對農業之影響,由於農業產品對氣候條件極為敏感,未來會因全球氣候變遷將會面對更多的挑戰,因此尋找一合適的氣候-作物模型則為重要的工作,傳統的作物生產函數利用生育度數法(Growing Degree Days,GDD),外生給定一適合作物生產的溫度門檻值,調查溫度與作物產量間的關係。然而,作物生長狀況與種類,會隨著不同的生長地帶而不盡相同,作物生長會隨著生產地的不同而有不同的溫度容忍度,可能會造成高估或低估作物生產量的問題,因此在此部分利用Panel Smooth Transition Regression model內生以2002年-2009年台灣不同地區(北部、中部、南部、東部)稻米產出為例找出一適合台灣稻米生產的溫度門檻值,並且利用此內生溫度門檻值重新計算GDD,估計作物生產函數。實證結果顯示溫度與台灣稻米產量間呈現非線性的關係,影響稻米生產的溫度門檻會隨著不同地區與耕種期間而不同。同時也發現利用內生的溫度門檻所計算出來的GDD對稻米產量的估計表現較傳統方法(利用外生的溫度門檻計算GDD)佳。
The greenhouse gas (hereafter GHG) emissions have sharply increased with the industrial development both in developed and developing countries. In particular, carbon dioxide (hereafter CO2) emissions account for over half of GHG emissions which is likely related to climate change. The Intergovernmental Panel on Climate Change (hereafter IPCC) report in 2007 indicated that the GHG emissions in 2030 will increase by 25-90% as compared with year 2000, and energy-related CO2 emissions in 2030 will increase by 40%-110%. Hence, how to face the threat of climate change has become the world-wide primary task. This part employs a panel cointegration and vector error-correction model to discuss the dynamic economy-energy-environment nexus for 188 countries from time period of 1993 to 2010. Moreover, considering the different level of economic development might induce divergent results. The empirical results indicate that there exist the long-run relationships between GDP, energy consumption, and CO2 emissions in developed, developing countries, and all over the world. The energy consumption has positively influenced CO2 emissions while the relationship between GDP and CO2 emissions are fitting the Environmental Kuznets Curve (EKC) hypothesis. For the short-run causality results, developed countries present that the unidirectional causality from energy consumption to CO2 emissions and GDP to energy consumption, and bidirectional causality between GDP and CO2 emissions. However, the unidirectional causality from energy consumption to CO2 emissions, and bidirectional causality between CO2 emissions and energy consumption with GDP exist in developing countries. Simultaneously, IPCC report in 2007 considered that the phenomenon of the sustained increase in global surface temperatures causes a higher frequency of heat waves, and predicted that extreme weather events will become more serious and frequent in the future. Hence, an amendment to conform the actual climatic thresholds for Human society is urgently required to help us cope with the uncertainty in our comprehension for the risks of the impacts of extreme weather events. First, we use the multiple panel threshold model to test whether there are threshold effects between temperature and mortality, using a panel of 78 major cities in 22 OECD countries for 1990-2008. From the empirical analysis, we find that the relationship between temperature and mortality has three threshold effects, namely 15.21℉ (-9.33℃), 46.97℉ (8.32℃), and 87.53℉ (30.85℃). If the temperature is below 15.21℉ (-9.33℃), the magnitude of the temperature effect below 15.21℉ (-9.33℃) is greater than the effect between 15.21℉ (-9.33℃) and 46.97℉ (8.32℃). When the temperature exceeds 87.53℉ (30.85℃), higher temperature leads to higher mortality rate. Based on the estimated coefficients of mean temperatures in four regimes, we separate 78 cities into five areas with latitudes below 30°, 31°-40°, 41°-50°, 51°-60°, and 61°-70°, and predict the impacts of future climate change on mortality for 2021-2040, 2041-2060, and 2061-2100. In summer, climate is predicted to increase mortality rates for 2021-2040, 2041-2060, and 2061-2100. For latitudes 41°-50° and 51°-60°, the rate of increases in mortality rates in summer induced by future climate change is much larger than for other latitudes mainly due to increases in mean temperature. In winter, the magnitude of mortality rate induced by future climate change is found to become smaller mainly due to increasing mean temperature and decreasing temperature variance. Next, this part discusses the impacts of climate threshold on crop production. Agricultural productions are sensitive to climate change and will encounter multifaceted challenges due to global climate change. Applying the appropriate climate-crop model on this issue is an imperative job. Traditional crop yield function using growing degree days method assumes exogenously temperature threshold to investigate the relationship between temperature and crop yield. However, such exogenously temperature threshold may be altered by climate change and will cause inconsistent outcomes if such approach is applied. This study uses a new approach, Panel Smooth Transition Regression Model, to estimate the endogenously temperature threshold and then applying this threshold back to the crop yield function. Rice yields for four regions in Taiwan during the period of 2002 to 2009 are applied. The empirical results show that that there exists the nonlinear temperature effects on rice crop and the related temperature thresholds for rice yield are also estimated. Identifying temperature thresholds could help us to quantify the impacts of different temperature regimes on rice yield. We discover that using the endogenously identified temperature thresholds for rice growth has better performance than the traditional approach.
其他識別: U0005-2907201314250000
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